Fine-tuning a deep network is also a popular approach for transfer learning [1]}. We also compared our proposed method with fine-tuning. Fine-tuning a ResNet-50 on Rottnest Island data achieved an overall classification accuracy of 58.8% as compared to the 59.0% achieved by our proposed method. For Benthoz15 dataset, fine-tuning a ResNet-50 resulted in an overall classification accuracy of 57.1% which is 0.5% lower than our proposed method. The performance change was marginal for both datasets. Hence, we concluded that the classification accuracy achieved by both methods on benthic marine datasets is comparable. One important aspect to compare is the computational time required by these two approaches. The time needed to extract off-the-shelf features from a ResNet and classify them using an SVM classifier is far less than the time required to fine-tune a 50 layer ResNet on a dataset as large as 297,800 input images. Our proposed method requires a few hours to run. However, fine-tuning a ResNet-50 with Rottnest Island dataset takes at least 2 days on an Nvidia Titan-X GPU. Given these considerations, we selected our proposed method over fine-tuning a ResNet with a marine dataset approach.
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